Object cognitive identification solution

ABSTRACT

Embodiments of the present invention provide a computer-implemented method for identifying a specified object. The method includes collecting attributes of a physical object within a physical space, in which the attributes include an object temperature obtained via an (IR) sensor and an object shape obtained via an image sensor. A machine learning algorithm is executed to allocate a coordinate to the object based on the attributes. In response to a request from a user to locate the object, the physical space is scanned via the IR sensor and the image sensor to identify the object and to detect a present location. An indication of a location of the object is then displayed to the user, in which the indication is at least one of the present location of the object or a predicted location of the object.

BACKGROUND

The present invention generally relates to object recognitiontechnology, and more specifically, to a cognitive-based objectidentification solution that can locate a target object according to itsattributes, without a specific need to add identifying markings on thesurface of the object.

People tend to forget or are unable to remember where physical objectsare located. This issue has been exacerbated over the last few yearsbecause the number of items found in the home has increased. As such,equipment used to find items within the home needs to improve in orderto locate objects in a manner that is both timely and accurate. Somesystems perform object recognition based on placing a marking on anobject such as a tag or label and then utilizing marking detectiontechnology such as RFID technology to detect the object within a certainphysical space. Some systems search and locate desired objects bypreloading a reference image of the desired object and then capturing areal-time captured image of the object to the preloaded reference image.

The phrase “machine learning” broadly describes a function of electronicsystems that learn from data. A machine learning system, engine, ormodule can include a trainable machine learning algorithm that can betrained, such as in an external cloud environment, to learn functionalrelationships between inputs and outputs that are currently unknown.

SUMMARY

Embodiments of the present invention provide a computer-implementedmethod for identifying a specified physical object based on a pluralityof attributes that are obtained via at least a plurality of sensors. Anon-limiting example of the computer-implemented method includescollecting the plurality of attributes of the specified physical objectthat is within a physical space, in which the plurality of attributesincludes a temperature of the specified physical object, and a shape ofthe specified physical object. The temperature of the specified physicalobject is obtained via one or more infrared (IR) sensors. The shape isobtained via one or more image sensors. The method includes executing amachine learning algorithm to allocate a coordinate to the specifiedphysical object based on the plurality of attributes, in which thecoordinate represents a location of the specified physical object withinthe physical space at the time of the collection of the plurality ofattributes. The method includes monitoring, a status of the specifiedphysical object within the physical space over a period of time via atleast the one or more IR sensors and the one or more image sensors, inwhich the monitoring of the status includes collecting coordinates ofthe specified physical object within the physical space to obtainlocations of the specified physical object within the physical spaceover the period of time. The method includes in response to receiving arequest from a user to locate the specified physical object, scanningthe physical space via the at least one or more IR sensors and the oneor more image sensors to identify the specified physical object withinthe physical space and to detect a present location of the specifiedphysical object within the physical space. The method includesdisplaying to the user, an indication of a location of the specifiedphysical object, in which the indication is at least one of the presentlocation of the specified physical object or a predicted location of thespecified physical object.

Embodiments of the present invention provide a system for identifying aspecified physical object based on a plurality of attributes that areobtained via at least a plurality of sensors. The system includes one ormore processors that are configured to perform a method. A non-limitingexample of the method includes collecting the plurality of attributes ofthe specified physical object that is within a physical space, in whichthe plurality of attributes includes a temperature of the specifiedphysical object, and a shape of the specified physical object. Thetemperature of the specified physical object is obtained via one or moreinfrared (IR) sensors. The shape is obtained via one or more imagesensors. The method includes executing a machine learning algorithm toallocate a coordinate to the specified physical object based on theplurality of attributes, in which the coordinate represents a locationof the specified physical object within the physical space at the timeof the collection of the plurality of attributes. The method includesmonitoring, a status of the specified physical object within thephysical space over a period of time via at least the one or more IRsensors and the one or more image sensors, in which the monitoring ofthe status includes collecting coordinates of the specified physicalobject within the physical space to obtain locations of the specifiedphysical object within the physical space over the period of time. Themethod includes in response to receiving a request from a user to locatethe specified physical object, scanning the physical space via the atleast one or more IR sensors and the one or more image sensors toidentify the specified physical object within the physical space and todetect a present location of the specified physical object within thephysical space. The method includes displaying to the user, anindication of a location of the specified physical object, in which theindication is at least one of the present location of the specifiedphysical object or a predicted location of the specified physicalobject.

Embodiments of the invention provide a computer program product foridentifying a specified physical object based on a plurality ofattributes that are obtained via at least a plurality of sensors, thecomputer program product comprising a computer readable storage mediumhaving program instructions embodied therewith. The program instructionsare executable by a system comprising one or more processors to causethe system to perform a method. A non-limiting example of the methodincludes collecting the plurality of attributes of the specifiedphysical object that is within a physical space, in which the pluralityof attributes includes a temperature of the specified physical object,and a shape of the specified physical object. The temperature of thespecified physical object is obtained via one or more infrared (IR)sensors. The shape is obtained via one or more image sensors. The methodincludes executing a machine learning algorithm to allocate a coordinateto the specified physical object based on the plurality of attributes,in which the coordinate represents a location of the specified physicalobject within the physical space at the time of the collection of theplurality of attributes. The method includes monitoring, a status of thespecified physical object within the physical space over a period oftime via at least the one or more IR sensors and the one or more imagesensors, in which the monitoring of the status includes collectingcoordinates of the specified physical object within the physical spaceto obtain locations of the specified physical object within the physicalspace over the period of time. The method includes in response toreceiving a request from a user to locate the specified physical object,scanning the physical space via the at least one or more IR sensors andthe one or more image sensors to identify the specified physical objectwithin the physical space and to detect a present location of thespecified physical object within the physical space. The method includesdisplaying to the user, an indication of a location of the specifiedphysical object, in which the indication is at least one of the presentlocation of the specified physical object or a predicted location of thespecified physical object.

Additional technical features and benefits are realized through thetechniques of the present invention. Embodiments and aspects of theinvention are described in detail herein and are considered a part ofthe claimed subject matter. For a better understanding, refer to thedetailed description and to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The specifics of the exclusive rights described herein are particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe embodiments of the invention are apparent from the followingdetailed description taken in conjunction with the accompanying drawingsin which:

FIG. 1 depicts a cloud computing environment according to one or moreembodiments of the present invention;

FIG. 2 depicts abstraction model layers according to one or moreembodiments of the present invention;

FIG. 3 depicts an exemplary computer system capable of implementing oneor more embodiments of the present invention;

FIG. 4 depicts an example distributed environment in accordance with oneor more embodiments of the present invention; and

FIG. 5 depicts a diagram illustrating an example methodology of amachine learning component in accordance with one or more embodiments ofthe present invention.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagram or the operations described therein withoutdeparting from the spirit of the invention. For instance, the actionscan be performed in a differing order or actions can be added, deleted,or modified. Also, the term “coupled” and variations thereof describeshaving a communications path between two elements and does not imply adirect connection between the elements with no interveningelements/connections between them. All of these variations areconsidered a part of the specification.

In the accompanying figures and following detailed description of thedisclosed embodiments, the various elements illustrated in the figuresare provided with two or three digit reference numbers. With minorexceptions, the leftmost digit(s) of each reference number correspond tothe figure in which its element is first illustrated.

DETAILED DESCRIPTION

Various embodiments of the invention are described herein with referenceto the related drawings. Alternative embodiments of the invention can bedevised without departing from the scope of this invention. Variousconnections and positional relationships (e.g., over, below, adjacent,etc.) are set forth between elements in the following description and inthe drawings. These connections and/or positional relationships, unlessspecified otherwise, can be direct or indirect, and the presentinvention is not intended to be limiting in this respect. Accordingly, acoupling of entities can refer to either a direct or an indirectcoupling, and a positional relationship between entities can be a director indirect positional relationship. Moreover, the various tasks andprocess steps described herein can be incorporated into a morecomprehensive procedure or process having additional steps orfunctionality not described in detail herein.

The following definitions and abbreviations are to be used for theinterpretation of the claims and the specification. As used herein, theterms “comprises,” “comprising,” “includes,” “including,” “has,”“having,” “contains” or “containing,” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, acomposition, a mixture, process, method, article, or apparatus thatcomprises a list of elements is not necessarily limited to only thoseelements but can include other elements not expressly listed or inherentto such composition, mixture, process, method, article, or apparatus.

Additionally, the term “exemplary” is used herein to mean “serving as anexample, instance or illustration.” Any embodiment or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. The terms “at least one”and “one or more” may be understood to include any integer numbergreater than or equal to one, i.e., one, two, three, four, etc. Theterms “a plurality” may be understood to include any integer numbergreater than or equal to two, i.e., two, three, four, five, etc. Theterm “connection” may include both an indirect “connection” and a direct“connection.”

The terms “about,” “substantially,” “approximately,” and variationsthereof, are intended to include the degree of error associated withmeasurement of the particular quantity based upon the equipmentavailable at the time of filing the application. For example, “about”can include a range of ±8% or 5%, or 2% of a given value.

For the sake of brevity, conventional techniques related to making andusing aspects of the invention may or may not be described in detailherein. In particular, various aspects of computing systems and specificcomputer programs to implement the various technical features describedherein are well known. Accordingly, in the interest of brevity, manyconventional implementation details are only mentioned briefly herein orare omitted entirely without providing the well-known system and/orprocess details.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems; storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 1, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms, and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 2 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and object recognition processing 96.

Turning now to an overview of technologies that are more specificallyrelevant to aspects of the invention, as noted above, people tend toforget or are unable to remember where physical objects are located.This issue has been exacerbated over the last few years because thenumber of items found in the home has increased. As such, equipment usedto find items within the home needs to improve in order to locateobjects in a manner that is both timely and accurate. Some systemsperform object recognition based on placing a marking on an object suchas a tag or label and then utilizing marking detection technology suchas RFID technology to detect the object within a certain physical space.Some systems search and locate desired objects by preloading a referenceimage of the desired object and then capturing a real-time capturedimage of the object to the preloaded reference image.

There are several difficulties involved in how traditional objectidentification systems operate. For example, some systems rely onpreloading a reference image before any object recognition process isperformed. Moreover, systems that rely on performing image analysisthrough the comparison of a preloaded image of a first object and areal-time captured image of a second object are often unable torecognize that the two objects are the same object when the images eachhave a different brightness, different viewpoints, different scales,different deformations of the object, different occlusions, and/ordifferent amounts of background noise.

Turning now to an overview of the aspects of the invention, one or moreembodiments of the invention address the above-described shortcomings ofthe prior art by providing a machine learning tool that is capable ofidentifying a specified object within a physical space such as a roomvia at least a plurality of detectors and/or sensors that are positionedon or within the physical space (e.g., sensors attached to walls of agiven space). In some embodiments of the present invention, any itemthat is detected by the sensors is recorded and identified by the toolbased on the item's detected appearance, material, temperature, and/orother main characteristics of the object, and then the data istransferred to a database. This data is analyzed by a trained and/orself-learning machine learning algorithm such that the tool is able toaccurately identify a location of the object within the physical spaceand alert the user based on certain conditions.

Different from prior approaches, in some embodiments of the presentinvention, the system does not need to make any marks or labels onobjects. Rather, a plurality of sensors are located within a physicalarea, in which items found in the physical area can be detected and thenshown in a display device for convenient identification.

In some embodiments of the present invention, a trained machine learningmodel learns the object's properties by searching an online source suchas a website using special characteristics and parameters of the objectand obtaining object properties from the online source in view of thesearch. The plurality of sensors can detect diverse characteristics ofobjects and be used to monitor a moving track of the object. Thesefeatures assist the system in identifying the object with the preciseposition. The system can also analyze the user's particular applicationscenarios to help recognize and locate the object. In some embodimentsof the present invention, a similarity comparison score is shown in adisplay screen for one or more choices of a sequence, such as from ahigh percentage score to a low percentage score (e.g., probabilityscore). In some embodiments of the present invention, feedback isobtained from the user to help the system learn and improve the accuracyof the analysis and results.

Turning now to a more detailed description of aspects of the presentinvention, FIG. 3 illustrates a high-level block diagram showing anexample of a computer-based system 300 that is useful for implementingone or more embodiments of the invention. Although one exemplarycomputer system 300 is shown, computer system 300 includes acommunication path 326, which connects computer system 300 to additionalsystems and may include one or more wide area networks (WANs) and/orlocal area networks (LANs) such as the internet, intranet(s), and/orwireless communication network(s). Computer system 300 and additionalsystems are in communication via communication path 326, (e.g., tocommunicate data between them).

Computer system 300 includes one or more processors, such as processor302. Processor 302 is connected to a communication infrastructure 304(e.g., a communications bus, cross-over bar, or network). Computersystem 300 can include a display interface 306 that forwards graphics,text, and other data from communication infrastructure 304 (or from aframe buffer not shown) for display on a display unit 308. Computersystem 300 also includes a main memory 310, preferably random accessmemory (RAM), and may also include a secondary memory 312. Secondarymemory 312 may include, for example, a hard disk drive 314 and/or aremovable storage drive 316, representing, for example, a floppy diskdrive, a magnetic tape drive, or an optical disk drive. Removablestorage drive 316 reads from and/or writes to a removable storage unit318 in a manner well known to those having ordinary skill in the art.Removable storage unit 318 represents, for example, a floppy disk, acompact disc, a magnetic tape, or an optical disk, etc., which is readby and written to by a removable storage drive 316. As will beappreciated, removable storage unit 318 includes a computer readablemedium having stored therein computer software and/or data.

In some alternative embodiments of the invention, secondary memory 312may include other similar means for allowing computer programs or otherinstructions to be loaded into the computer system. Such means mayinclude, for example, a removable storage unit 320 and an interface 322.Examples of such means may include a program package and packageinterface (such as that found in video game devices), a removable memorychip (such as an EPROM or PROM) and associated socket, and otherremovable storage units 320 and interfaces 322 which allow software anddata to be transferred from the removable storage unit 320 to computersystem 300.

Computer system 300 may also include a communications interface 324.Communications interface 324 allows software and data to be transferredbetween the computer system and external devices. Examples ofcommunications interface 324 may include a modem, a network interface(such as an Ethernet card), a communications port, or a PCM-CIA slot andcard, etc. Software and data transferred via communications interface324 are in the form of signals which may be, for example, electronic,electromagnetic, optical, or other signals capable of being received bycommunications interface 324. These signals are provided tocommunications interface 324 via communication path (i.e., channel) 326.Communication path 326 carries signals and may be implemented using awire or cable, fiber optics, a phone line, a cellular phone link, an RFlink, and/or other communications channels.

In the present disclosure, the terms “computer program medium,”“computer usable medium,” and “computer readable medium” are used togenerally refer to media such as main memory 310 and secondary memory312, removable storage drive 316, and a hard disk installed in hard diskdrive 314. Computer programs (also called computer control logic) arestored in main memory 310, and/or secondary memory 312. Computerprograms may also be received via communications interface 324. Suchcomputer programs, when run, enable the computer system to perform thefeatures of the present disclosure as discussed herein. In particular,the computer programs, when run, enable processor 302 to perform thefeatures of the computer system. Accordingly, such computer programsrepresent controllers of the computer system.

Referring now to FIG. 4, an example distributed environment 400 ispresented for locating specified objects based on the object'sattributes using a series of detectors and/or sensors. Distributedenvironment 400 includes one or more user devices 402, an objectdetection system 404, and a plurality of sensor 408, which areinterconnected via network 406. FIG. 4 provides an illustration of onlyone example system and does not imply any limitation with regard toother systems in which different embodiments of the present inventionmay be implemented. Various suitable modifications to the depictedenvironment may be made, by those skilled in the art, without departingfrom the scope of the invention as recited by the claims. For example,in some embodiments of the present invention, the object detectionsystem includes the plurality of sensors 408.

Object detection system 404 includes a communication and displaycomponent 410, a machine learning component 412, and a database 414. Insome embodiments of the present invention, communication and displaycomponent 410, machine learning component 412, and/or database 414 areinterconnected via a communication infrastructure 304 and/orcommunication path 326. Object detection system 404 may have internaland external hardware components, such as those depicted and describedabove with respect to FIG. 3.

Object detection system 404 is a machine learning system that can beutilized to solve a variety of technical issues (e.g., learningpreviously unknown functional relationships) in connection withtechnologies such as, but not limited to, machine learning technologies,video processing technologies, object generalization technologies, dataanalytics technologies, data classification technologies, dataclustering technologies, recommendation system technologies, signalprocessing technologies, and/or other digital technologies. Objectdetection system 404 employs hardware and/or software to solve problemsthat are highly technical in nature, that are not abstract and thatcannot be performed as a set of mental acts by a human.

Various suitable machine learning algorithms may be used to extractobject properties, identify objects, identify commonalities betweenobject clusters or categories, or generate similarity scores. In oneexample, a Watson API service may be used to implement and/or call amachine learning algorithm that analyzes the object properties and/orfeedback. The following are non-limiting examples of cognitive APIs thatmay be used: Alchemy Language Icon Alchemy Language, ConversationService Icon Conversation, Dialog Icon Dialog (Deprecated), DocumentConversion Icon Document Conversion, Language Translator Icon LanguageTranslator, Natural Language Classifier Icon Natural LanguageClassifier, Natural Language Understanding Icon Natural LanguageUnderstanding, Personality Insights Icon Personality Insights, Retrieveand Rank Icon Retrieve and Rank, Tone Analyzer Icon Tone Analyzer,Visual Recognition Icon Visual Recognition, Discovery Icon Discovery,and Tradeoff Analytics Icon Tradeoff Analytics.

Machine learning is often employed by numerous technologies to determineinferences and/or relationships among digital data such as sensor data.For example, machine learning technologies, signal processingtechnologies, image processing technologies, data analysis technologies,and/or other technologies employ machine learning models to analyzedigital data, process digital data, determine inferences from digitaldata, and/or determine relationships among digital data. Machinelearning functionality can be implemented using an artificial neuralnetwork (ANN) having the capability to be trained to perform a currentlyunknown function. In machine learning and cognitive science, ANNs are afamily of statistical learning models inspired by the biological neuralnetworks of animals, and in particular the brain. ANNs can be used toestimate or approximate systems and functions that depend on a largenumber of inputs.

ANNs can be embodied as so-called “neuromorphic” systems ofinterconnected processor elements that act as simulated “neurons” andexchange “messages” between each other in the form of electronicsignals. Similar to the so-called “plasticity” of synapticneurotransmitter connections that carry messages between biologicalneurons, the connections in ANNs that carry electronic messages betweensimulated neurons are provided with numeric weights that correspond tothe strength or weakness of a given connection. The weights can beadjusted and tuned based on experience, making ANNs adaptive to inputsand capable of learning. For example, an ANN for handwriting recognitionis defined by a set of input neurons that can be activated by the pixelsof an input image. After being weighted and transformed by a functiondetermined by the network's designer, the activation of these inputneurons are then passed to other downstream neurons, which are oftenreferred to as “hidden” neurons. This process is repeated until anoutput neuron is activated. The activated output neuron determines whichcharacter was read.

In certain embodiments of the invention, some or all of the processesperformed by object detection system 404 are performed by one or morespecialized computers for carrying out defined tasks related to machinelearning. In some embodiments of the invention, object detection system404 and/or components of the system are employed to solve new problemsthat arise through advancements in technologies mentioned above.

In general, object detection system 404 is a cognitive-based tool that,in some embodiments of the present invention, is configured to locateone or more specified objects within a physical area based on objectattributes using a plurality of detectors and/or sensors 408 without aspecific need to add labels, barcodes, or tags on the objects.

For example, in some embodiments of the present invention, the systemutilizes integrated sensors 408 to automatically capture an object'smultiple properties that include more than just image based properties(e.g., via communication and display component 410), and to locate theobject according to its attributes without adding labels, barcodes,fingerprints, or tags to the objects. In some embodiments of the presentinvention, there is no need for the system to preload specific data of atarget object or scan through a whole object at the beginning of theprocess to obtain a reference image. Rather, in some embodiments of thepresent invention, the system is able to access the internet to searchfor data relating to the object (e.g., characteristics, parameters,image(s), and description of the object), which assists the system inbeing able to recognize and locate object via machine learning (e.g.,via machine learning component 412). For example, in some embodiments ofthe present invention, a user can provide the system with a request thatincludes the name of an object, and the system can search online fordata that defines this object.

In some embodiments of the present invention, object detection system404 is configured to monitor and record in real time an object's status,location, and/or historical movement track for one or more differentobjects found within a physical space (e.g. via sensors 408 andcommunication and display component 410). In some embodiments of thepresent invention, the physical space is an indoor environment such as ahome, shopping mall, warehouse, office space, etc. In some embodimentsof the present invention, the physical space is an outdoor environmentsuch as a street or park. Object detection system 404 then maps thehistorical movement track and provides an answer to the user as to wherethe object is located in the physical space or provides a suggestedlocation even when object detection system 404 is unable to detect theactual location of the object (e.g., via communication and displaycomponent 410). In some embodiments of the present invention, objectdetection system 404 records and learns different object usageenvironments according to a particular user's habits (e.g., via machinelearning component 412), which helps object detection system 404recognize the object and increase the accuracy of judgements made byobject detection system 404 (e.g., recognition of the object, detectionof the actual location of the object, predicting a location of theobject, monitoring the status of the object, etc.).

In some embodiments of the present invention, object detection system404 is configured to distinguish between two or more similar items toidentify which of the two or more items is, in fact, the user's desiredtarget item. In some embodiments of the present invention, the systemdistinguishes between the two or more similar items based on one or morefactors such as, for example, the target item's attributes and real-timetrack, the target item's usage environment, the user's usage habit ofthe item, and/or attributes of the item obtained through the internet(e.g., the item's description, characteristics, parameters, etc.).

In some embodiments of the present invention, object detection system404 allows for sustainable self-trained machine learning which improvesthe accuracy rate of finding and/or judging the required items as objectdetection system 404 becomes more familiar with the user's object usageenvironment, and user's habits, and/or the user's feedback (e.g., viamachine learning component 412).

In some embodiments of the present invention, object detection system404 includes an alert function that is configured to notify the userupon detecting the object is located in a risky area of the physicalspace (e.g., the item is almost falling off a table) and/or to reportthe object status to the user. One example benefit that stems from theuse of the plurality of sensors is that object detection system 404 isable collect a plurality of attributes of the object (e.g., 3Ddimension, color, temperature, etc.), calculate the barycenter of theobject, and then decide whether the object is likely to fall off apresent surface (i.e., risky area) based on the calculated barycenter,and/or estimate whether the object is presently in an abnormal statuscondition (e.g., the item is presently overheated).

In some embodiments of the present invention, object detection system404 is a standalone computing device, a management server, a web server,a mobile computing device, or other suitable electronic device and/orcomputing system capable of receiving, sending, and processing data. Insome embodiments of the present invention, object detection system 404is a server computing system utilizing multiple computers, such as incloud computing environment 50. In some embodiments of the presentinvention, object detection system 404 is a laptop computer, a tabletcomputer, a netbook computer, a personal computer (PC), a desktopcomputer, a personal digital assistant (PDA), a smartphone, or othersuitable programmable electronic device capable of communicating withuser device 402, sensors 408, and other computing devices (not shown)within distributed environment 400 via network 406. In some embodimentsof the present invention, object detection system 404 is a computingsystem utilizing clustered computers and components (e.g., databaseserver computers, application server computers, etc.) that act as asingle pool of seamless resources that are accessible within distributedenvironment 400. Object detection system 404 may have internal andexternal hardware components, such as those depicted and described abovewith respect to FIG. 3.

Network 406 can be, for example, a telecommunications network, a localarea network (LAN), a wide area network (WAN), such as the Internet, ora combination of the three, and can include wired, wireless, or fiberoptic connections. Network 406 can include one or more wired and/orwireless networks that are capable of receiving and transmitting data,voice, and/or video signals, including multimedia signals that includevoice, data, and video information. In general, network 406 can be anysuitable combination of connections and protocols that can supportcommunications between user device 402, sensor 408, object detectionsystem 404, and/or other computing devices (not shown) within adistributed environment 400. In some embodiments of the presentinvention, distributed environment 400 is implemented as part of a cloudcomputing environment such as cloud computing environment 50 (FIG. 1).

User device 402 is configured to allow to send and/or receiveinformation from user device 402 to object detection system 404 and/orsensors 408, which in turn allows a user access to communication anddisplay component 410, machine learning component 412, and/or database414. In some embodiments of the present invention, database 414 isconfigured to store data that is obtained from user device 402, sensors408, an online source and/or object detection system 404 (e.g.,attribute data, a matching list, and/or machine learning features).

In some embodiments of the present invention, user device 402 isconfigured to gather user input data, audible data, and/or visual data.In some embodiments of the present invention, user device 402 isconfigured to capture audio, images, and/or video of the user (e.g., viaa microphone and/or camera of user device 402). In some embodiments ofthe present invention, user device 402 includes a GPS device fortracking a location of the user device 402. In some embodiments of thepresent invention, user device 402 executes one or more applications topresent indications and alerts, such as an actual location of a detectedobject within a physical space or a suggested location of an objectwithin a physical space. In some embodiments of the present invention,the indications and/or alerts are presented via a display device, aspeaker, and/or other single or combinations of output devices. In someembodiments of the present invention, user device 402 allows the user toprovide feedback to object detection system 404.

In some embodiments of the present invention, user device 402 is alaptop computer, a tablet computer, a netbook computer, a personalcomputer (PC), a desktop computer, a personal digital assistant (PDA), asmartphone, an internet-of-things (IoT) enabled device, and/or othersuitable programmable electronic devices capable of communicating withvarious components and devices within distributed environment 400. Insome embodiments of the present invention, user device 402 is aprogrammable electronic mobile device or a combination of programmableelectronic mobile devices capable of executing machine readable programinstructions and communicating with other computing devices (not shown)within distributed environment 400. In some embodiments of the presentinvention, user device 402 may include internal and external hardwarecomponents, such as those depicted and described above with respect toFIG. 3.

Sensors 408 are a plurality of sensors that are utilized to collectphysical attributes of an object found in a physical area. In someembodiments of the present invention, sensors 408 include one or moreimage sensors and one or more infrared (IR) sensors (e.g., near-infraredsensor). Various suitable types of image sensors may be utilized inaccordance with one or more embodiments of the present invention. Forexample, in some embodiments of the present invention, one or more ofthe image sensors are a three dimensional (3D) image sensor, a camera, aVR camera, a charge-coupled device (CCD) and/or a hyperspectral imagingdevice. In some embodiments of the present invention, the one or moreimage sensors are configured to collect attributes of objects such asshape (e.g., 2D or 3D shape), colors, dimensions, textures and/ormaterials of objects. In some embodiments of the present invention, theshape, colors, dimensions, textures, and/or materials of the object areobtained based, at least in part on, properties that are extracted froma 3D image of the physical space that captured by a 3D image sensor. Insome embodiments of the present invention, the IR sensor are configuredto collect attributes of objects such as a temperature of an object, adensity of an object, and/or a luminosity of an object. Various suitablesensors 408 as known to those having ordinary skill in the art may beutilized in one or more embodiments of the present invention to obtain aplurality of attributes of the object.

In the example depicted in FIG. 4, object detection system 404 isconfigured to obtain a name of a desired physical object from a user andthen collect a plurality of attributes of the desired physical object.In some embodiments of the present invention, the plurality ofattributes are obtained via a plurality of sensors 408 that are locatedwithin a physical area. In some embodiments of the present invention,the collecting of attributes includes collecting from the physical areatemperature(s) of the object, color(s) of the object, material(s) of theobject, texture(s) of the object, and/or shape(s) of the object (e.g.,2D shape, 3D shape, etc.). In some embodiments of the present invention,the temperature of the object is obtained via one or more IR sensors ofthe plurality of sensors 408. In some embodiments of the presentinvention, the color and shape of the object are obtained via one ormore image sensors of the plurality of sensors 408. In some embodimentsof the present invention, the luminosity of the object, the density ofthe object, and/or dimensions of the object are obtained by theplurality of sensors 408. In some embodiments of the present invention,the plurality of attributes further includes a set of attributes of theobject that are obtained from an online source such as a website. Forexample, in some embodiments of the present invention, object detectionsystem 404 is configured to search a website to retrievecharacteristics, parameters, and/or a description associated with theobject. In some embodiments of the present invention, the search isconducted based on the name of the desired object, which is obtainedfrom the user.

In the context of locating a phone, for example, a user may enter a nameof a particular model of phone (e.g., brand name of a phone) or ageneric placeholder (e.g., “Mobile Phone”) and then object detectionsystem 404 collects a plurality of attributes pertaining to the phonesuch as, for example, that the phone has a particular 3D shape (e.g.,rectangular), has one or more particular colors (e.g., black and gold),is made of one or more particular materials (e.g., plastic and glass),and/or has a certain temperature or range of temperatures (e.g., a topportion of the phone being a first temperature whereas a bottom portionof the phone being a second different temperature). In some embodimentsof the present invention, all or some of the attributes above areobtained via sampling of data using the plurality of sensors 408 (e.g.,from image sensor data, IR sensor data, etc.). In some embodiments ofthe present invention, all or some of the attributes above are collectedby searching an online source, in which a machine learning model islearned (e.g., via machine learning component 412) based on the onlineavailable information such as, for example, dimensions of a particularmodel of phone, a standard temperature range of a particular model ofphone, and/or other suitable characteristics, parameters, ordescriptions of the phone.

In some embodiments of the present invention, object detection system404 is configured to analyze the attributes that were obtained by thesensors 408 and/or from the online source and to convert the data intomachine language such that object detection system 404 can allocate acoordinate (location) to the object. For example, in some embodiments ofthe present invention, object detection system 404 is configured toexecute a machine learning algorithm to allocate a coordinate to theobject based on the plurality of attributes object (e.g., via machinelearning component 412), in which the coordinate represents a locationof the object within the physical space at the time of the collection ofthe plurality of attributes (e.g., a location of the object within thephysical space as detected via the plurality of sensors).

In some embodiments of the present invention, object detection system404 executes the machine learning algorithm by processing a machinelearning model that is based on features that are extracted fromcollected attributes and/or feedback. In some embodiments of the presentinvention, object detection system 404 employs parallel computing toprocess portions of the collected attributes and/or feedback torecognize and identify a target object. For instance, in someembodiments of the invention, object detection system 404 performsparallel computing associated with two or more processors that processone or more portions of collected attributes and/or feedback inparallel. In one example, object detection system 404 executes aclassification machine learning model using the features extracted fromcollected attributes and/or feedback to identify an actual location ofthe object, suggest a predicted location of the object, and/or generatea probability score. In some embodiments of the present invention, thecollected attributes and/or feedback data are obtained from database414, sensors 408, an online source, and/or from user device 402. In someembodiments of the present invention, the classification machinelearning model maps extracted features to one or more categories. Inanother example, object detection system 404 executes a regressionmachine learning model using extracted features. In some embodiments ofthe present invention, a regression machine learning model is used todetermine relationships among collected attributes and/or feedback. Inyet another example, object detection system 404 executes a clusteringmachine learning model using a feature matrix that is populated based,at least in part, on the features that are extracted from collectedattributes and/or feedback. In some embodiments of the presentinvention, the execution of the machine learning algorithm includesgenerating a matching list of objects based on the extracted attributesand recording the list in database 414.

In some embodiments of the present invention, object detection system404 is configured to monitor a status of the object while the object iswithin the physical space, in which the monitoring of the statusincludes collecting coordinates of the object within the physical spaceto obtain locations of the object within the physical space. In someembodiments of the present invention, the monitoring is performedcontinuously over a predetermined period of time (e.g., hours, days,months, years,) or over an indefinite period of time, in which themonitoring is performed such that a status of the object is assessed atvarious instances in time. In some embodiments of the present invention,the monitoring of the status of the object is performed by collectingattributes of the object via at least one or more IR sensors and one ormore image sensors. Data obtained from the monitoring is then utilizedto update the machine learning algorithm and/or the attributesassociated with the object in the matching list of objects stored indatabase 414.

In some embodiments of the present invention, the monitoring of thestatus of the object includes tracking the object's use environment andthe object's frequent position within the environment (e.g., thefrequency of position within a home). In some embodiments of the presentinvention, the monitoring includes generating a historical movementtrack of the object based on the collected coordinates. In someembodiments of the present invention, the historical movement trackrepresents a plurality of locations of the object with associated pointsin time.

In some embodiments of the present invention, the monitoring of thestatus of the object includes detecting during the period of time whenthe object is located in a risky area (e.g., detecting that the objectis likely to fall off of a surface). In some embodiments of the presentinvention, object detection system 404 is configured to detect when theobject is located in a risky area by calculating a barycenter of theobject based on the dimensions of the object (e.g., identifying thecenter of mass), measuring a distance between a predetermined locationand the barycenter of the object, and then transmitting an alert to theuser in response to detecting that the distance is less than a thresholddistance (e.g., via communication and display component 410), in whichthe alert includes an indication of a location of the object. Forexample, if object detection system 404 is configured to avoid the riskof objects falling off a table, in some embodiments of the presentinvention, object detection system 404 is configured to transmit analert when the object is detected as being positioned on a surface ofthe table while the barycenter of the object is positioned within acertain predetermined distance from the edge of the table (e.g.,barycenter is detected as being n inches past the edge of the table,barycenter is detected as being n inches before the edge of the table).

In some embodiments of the present invention, the monitoring of thestatus of the object includes detecting during the period of time whenthe object is in an abnormal state (e.g., the object is overheated). Insome embodiments of the present invention, object detection system 404is configured to detect when the object is in an abnormal state bymonitoring the temperature of the object over the period of time via theone or more IR sensors, and transmitting an alert to the user indicatinga location of the object when object detection system 404 detects thatthe temperature of the object is above a predetermined maximumtemperature and/or below a predetermined minimum temperature. Forexample, if object detection system 404 detects that the phone foundwithin a physical space has a temperature that exceeds a maximumrecommended operating temperate as established by the specification ofthe phone (e.g., obtained via an online source), then in someembodiments of the present invention an alert is triggered. This mightoccur for example when the phone is positioning in an unsafe locationsuch as such as a surface of a stove in the kitchen.

In some embodiments of the present invention, object detection system404 is configured to receive requests from users regarding a desiredobject. For example, in some embodiments of the present invention,object detection system 404 is configured to receive a user request tolocate a particular object. Object detection system 404 is configured torecognize the object as being within the physical space and to detect apresent location of the object within the physical space. In someembodiments of the present invention, object detection system 404recognizes the requested object and identifies the location of therequested object by obtaining attributes of the requested object fromthe matching list stored in database 414, scanning the physical spaceusing the plurality of sensors to collect attributes of one or moreobjects found within the physical space, and then utilizing the machinelearning algorithm to match the attributes of the request object to theattributes collected.

In some embodiments of the present invention, the requested object isidentified further based on historical movement track of the object. Forexample, consider a scenario where there are two or more kinds ofsimilar appliance controllers within a home such as a TV controller andan air conditioning controller, in which attributes of both controllersare similar and thus difficult to distinguish. Object detection system404 may distinguish the two controllers by, for example, tracking themovement of the various controllers over time and then learning viamachine learning component 412 from the movement track where the twodifferent controllers tend to show up most often. Accordingly, if forexample a historical movement track of the TV controller establishesthat the TV controller is most often found within the living room areaand that the historical movement track of the air conditioningcontroller establishes that the air conditioning controller is mostoften found in a bedroom area, object detection system 404 would thenidentify which of the two controllers is the requested controller bydistinguishing between the two controllers based on the two historicalmovement tracks. In some embodiments of the present invention, objectdetection system 404 is further configured to distinguish between twosimilar objects based on the object's usage environment and the user'susing habit of the object within the usage environment as learned by themachine learning algorithm. In some embodiments of the presentinvention, object detection system 404 is configured to distinguishbetween two similar objects further based on the description,characteristics, and/or parameters of the object that are learned froman online source via machine learning.

In some embodiments of the present invention, if object detection system404 is unable to detect the present location of the desired objectwithin the physical space, object detection system 404 is generates apredicted location of the object to suggest a potential location ofwhere the object may be positioned within or outside the physical space.This may occur for example when attributes associated with a particularobject cannot be found presently within the physical area by theplurality of sensors (e.g., the object is hidden from view, object islocated outside of the physical area). In some embodiments of thepresent invention, the predicted location is generated based on thehistorical movement track of the object. For example, in a scenariowhere object detection system 404 the plurality of sensors 408 areunable to detected a TV controller within a living room and if thehistorical movement track of the TV controller establishes that TVcontroller is most often located on a top surface of a sofa, objectdetection system 404 may suggest that the TV controller is located underthe sofa.

In some embodiments of the present invention, object detection system404 is configured to display to the user, an indication of a location ofthe object in response to the received request, in which the indicationincludes the present location of the object (if available) or thepredicted location of the object. In some embodiments of the presentinvention, the indication is displayed to the user by superimposing thepresent location of the object on an image of the physical space. Insome embodiments of the present invention, the indication is a graphicalobject that is superimposed over the present location of the object orthe predicted location of the object (e.g., a graphical box, star,circle, text, etc.) or an outline of the object.

In some embodiments of the present invention, the indication displayedto the user further includes the historical movement track superimposedon the image of the physical space. For example, in some embodiments ofthe present invention, a line is drawn on an image of a physical space,such as a living room of a house, in which the line tracks the variouscaptured locations of the object over time. In some embodiments of thepresent invention, the indication further includes a probability scoreassociated with a likelihood that the predicted location of the objectis correct. For example, in the scenario above regarding the location ofa mobile phone, if object detection system 404 establishes via thehistorical movement track that the phone was located on a desk in abedroom at a first point in time and then located on a sofa in theliving room at a second point in time, object system 404 may establishvia the machine learning that the phone has an 80% probability of beingunder the sofa and a 20% probability of being in the bedroom because thesofa was the last place the phone was detected.

In some embodiments of the present invention, after displaying theindication to the user, object detection system 404 receives feedbackfrom the user regarding whether the object was in fact located at theindicated location. In some embodiments of the present invention, themachine learning algorithm and/or machine learning model is then updatedbased on the received feedback to improve the accuracy of results.

Additional details of the operation of object detection system 404 willnow be described with reference to FIG. 5, wherein FIG. 5 depicts a flowdiagram illustrating a methodology 500 according to one or moreembodiments of the present invention. At 502, a plurality of attributesare obtained of a specified physical object that is within a physicalspace. The plurality of attributes includes a temperature of the object,a color of the object, and a shape of the object, in which thetemperature of the object is obtained via one or more IR sensors, inwhich the color and shape are obtained via one or more image sensors. At504, a machine learning algorithm is executed to allocate a coordinateto the object based on the plurality of attributes, in which thecoordinate represents a location of the object within the physical spaceat a time of the collection of the plurality of attributes. At 506, astatus of the object within the physical space is obtained over a periodof time via at least the one or more IR sensors and the one or moreimage sensors. The monitoring of the status includes collectingcoordinates of the object within the physical space to obtain locationsof the object within the physical space over the period of time. At 508,the physical space is scanned in response to receiving a request from auser to locate the object, in which the physical space is scanned via atleast the one or more IR sensors and the one or more image sensors toidentify the object within the physical space and to detect a presentlocation of the object within the physical space. At 510, an indicationof a location of the object is displayed, in which the indicationcomprises at least one of the present location of the object or apredicted location of the object.

In some embodiments of the present invention, the monitoring of thestatus of the object further includes generating a historical movementtrack of the object based on the collected coordinates. In someembodiments of the present invention, the methodology includesgenerating a predicted location of the object based on the historicalmovement track of the object in response to failing to detect thepresent location of the object, in which the indication that isdisplayed to the user is superimposed on an image of the physical space,and in which the indication includes the predicted location of theobject. In some embodiments of the present invention, the indicationdisplayed to the user further includes the historical movement track anda probability score associated with the predicted location of theobject.

In some embodiments of the present invention, the plurality ofattributes further includes a set of attributes of the object that areobtained from an online source, in which the generating of the predictedlocation of the object is further based on the set of attributesobtained from the online source.

In some embodiments of the present invention, the monitoring of thestatus of the object further includes calculating a barycenter of theobject based on dimensions of the object, measuring a distance between apredetermined location and the barycenter of the object, andtransmitting an alert to the user in response to detecting that thedistance between the two is less than a threshold distance. In someembodiments of the present invention, the alert includes an indicationof the location of the object.

In some embodiments of the present invention, the monitoring of thestatus of the object further includes monitoring the temperature of theobject over the period of time, and transmitting an alert to the userindicating a location of the object in response to detecting that thetemperature of the object is above a predetermined maximum temperatureor below a predetermined minimum temperature. In some embodiments of thepresent invention, the alert includes an indication of the location ofthe object.

In some embodiments of the present invention, the methodology furtherincludes receiving feedback from the user regarding whether the objectwas in fact at the indicated location and then updating the machinelearning algorithm based on the results of the received feedback.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instruction by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdescribed herein.

What is claimed is:
 1. A computer-implemented method for identifying aspecified physical object based on a plurality of attributes that areobtained via at least a plurality of sensors, the computer-implementedmethod comprising: collecting, by a system comprising one or moreprocessors, the plurality of attributes of the specified physical objectwithin a physical space, wherein the plurality of attributes includes atemperature of the specified physical object and a shape of thespecified physical object, wherein the temperature of the specifiedphysical object is obtained via one or more infrared (IR) sensors,wherein the shape of the specified physical object is obtained via oneor more image sensors; executing, by the system, a machine learningalgorithm to allocate a coordinate to the specified physical objectbased on the plurality of attributes, wherein the coordinate representsa location of the specified physical object within the physical space ata time of the collection of the plurality of attributes; monitoring, bythe system, a status of the specified physical object within thephysical space over a period of time via at least the one or more IRsensors and the one or more image sensors, wherein the monitoring of thestatus includes collecting coordinates of the specified physical objectwithin the physical space to obtain locations of the object within thephysical space over the period of time; in response to receiving arequest from a user to locate the specified physical object, scanning,by the system, the physical space via at least the one or more IRsensors and the one or more image sensors to identify the specifiedphysical object within the physical space and to detect a presentlocation of the specified physical object within the physical space; anddisplaying, by the system, to the user, an indication of a location ofthe specified physical object, wherein the indication comprises at leastone of the present location of the specified physical object or apredicted location of the specified physical object.
 2. Thecomputer-implemented method of claim 1, wherein the monitoring furtherincludes generating a historical movement track of the specifiedphysical object based on the collected coordinates, wherein the methodfurther comprises: in response to failing to detect the present locationof the specified physical object, generating the predicted location ofthe specified physical object based on the historical movement track ofthe specified physical object, wherein the indication that is displayedto the user is superimposed on an image of the physical space, whereinthe indication includes the predicted location of the specified physicalobject.
 3. The computer-implemented method of claim 2, wherein theindication displayed to the user further includes the historicalmovement track and a probability score associated with the predictedlocation of the specified physical object.
 4. The computer-implementedmethod of claim 2, wherein the plurality of attributes further includesa set of attributes of the specified physical object obtained from anonline source, wherein the generating of the predicted location of thespecified physical object is further based on the set of attributesobtained from the online source.
 5. The computer-implemented method ofclaim 2, wherein the plurality of attributes further includes dimensionsof the specified physical object, wherein the monitoring of the statusof the specified physical object further includes: calculating abarycenter of the object based on the dimensions of the specifiedphysical object; measuring a distance between a predetermined locationand the barycenter of the specified physical object; and transmitting analert to the user in response to detecting that the distance is lessthan a threshold distance, wherein the alert includes an indication of alocation of the specified physical object.
 6. The computer-implementedmethod of claim 2, wherein the monitoring of the status of the specifiedphysical object further includes: monitoring the temperature of thespecified physical object over the period of time; and in response todetecting that the temperature of the specified physical object is abovea predetermined maximum temperature or below a predetermined minimumtemperature, transmitting an alert to the user indicating a location ofthe specified physical object.
 7. The computer-implemented method ofclaim 1 further comprising: receiving feedback from the user regardingwhether the specified physical object was at the indicated location; andupdating the machine learning algorithm based on the received feedback.8. A computer program product for identifying a specified physicalobject based on a plurality of attributes that are obtained via at leasta plurality of sensors, the computer program product comprising acomputer readable storage medium having program instructions embodiedtherewith, the program instructions executable by a system comprisingone or more processors to cause the system to perform a method, themethod comprising: collecting, by the system, the plurality ofattributes of the specified physical object within a physical space,wherein the plurality of attributes includes a temperature of thespecified physical object and a shape of the specified physical object,wherein the temperature of the specified physical object is obtained viaone or more infrared (IR) sensors, wherein the shape of the specifiedphysical object is obtained via one or more image sensors; executing, bythe system, a machine learning algorithm to allocate a coordinate to thespecified physical object based on the plurality of attributes, whereinthe coordinate represents a location of the specified physical objectwithin the physical space at a time of the collection of the pluralityof attributes; monitoring, by the system, a status of the specifiedphysical object within the physical space over a period of time via atleast the one or more IR sensors and the one or more image sensors,wherein the monitoring of the status includes collecting coordinates ofthe specified physical object within the physical space to obtainlocations of the specified physical object within the physical spaceover the period of time; in response to receiving a request from a userto locate the specified physical object, scanning, by the system, thephysical space via at least the one or more IR sensors and the one ormore image sensors to identify the specified physical object within thephysical space and to detect a present location of the specifiedphysical object within the physical space; and displaying, by thesystem, to the user, an indication of a location of the specifiedphysical object, wherein the indication comprises at least one of thepresent location of the specified physical object or a predictedlocation of the specified physical object.
 9. The computer programproduct of claim 8, wherein the monitoring further includes generating ahistorical movement track of the specified physical object based on thecollected coordinates, wherein the method further comprises: in responseto failing to detect the present location of the specified physicalobject, generating the predicted location of the specified physicalobject based on the historical movement track of the specified physicalobject, wherein the indication that is displayed to the user issuperimposed on an image of the physical space, wherein the indicationincludes the predicted location of the specified physical object. 10.The computer program product of claim 9, wherein the indicationdisplayed to the user further includes the historical movement track anda probability score associated with the predicted location of thespecified physical object.
 11. The computer program product of claim 9,wherein the plurality of attributes further includes a set of attributesof the specified physical object obtained from an online source, whereinthe generating of the predicted location of the specified physicalobject is further based on the set of attributes obtained from theonline source.
 12. The computer program product of claim 9, wherein theplurality of attributes further includes dimensions of the specifiedphysical object, wherein the monitoring of the status of the specifiedphysical object further includes: calculating a barycenter of the objectbased on the dimensions of the specified physical object; measuring adistance between a predetermined location and the barycenter of thespecified physical object; and transmitting an alert to the user inresponse to detecting that the distance is less than a thresholddistance, wherein the alert includes an indication of a location of thespecified physical object.
 13. The computer program product of claim 9,wherein the monitoring of the status of the specified physical objectfurther includes: monitoring the temperature of the specified physicalobject over the period of time; and in response to detecting that thetemperature of the specified physical object is above a predeterminedmaximum temperature or below a predetermined minimum temperature,transmitting an alert to the user indicating a location of the specifiedphysical object.
 14. The computer program product of claim 8, whereinthe method further comprises: receiving feedback from the user regardingwhether the object was at the indicated location; and updating themachine learning algorithm based on to the received feedback.
 15. Asystem for identifying a specified physical object based on a pluralityof attributes that are obtained via at least a plurality of sensors, thesystem comprising one or more processors configured to perform a method,the method comprising: collecting, by the system, the plurality ofattributes of the specified physical object within a physical space,wherein the plurality of attributes includes a temperature of thespecified physical object, and shape of the specified physical object,wherein the temperature of the specified physical object is obtained viaone or more infrared (IR) sensors, wherein the shape of the specifiedphysical object is obtained via one or more image sensors; executing, bythe system, a machine learning algorithm to allocate a coordinate to thespecified physical object based on the plurality of attributes, whereinthe coordinate represents a location of the specified physical objectwithin the physical space at a time of the collection of the pluralityof attributes; monitoring, by the system, a status of the specifiedphysical object within the physical space over a period of time via atleast the one or more IR sensors and the one or more image sensors,wherein the monitoring of the status includes collecting coordinates ofthe specified physical object within the physical space to obtainlocations of the specified physical object within the physical spaceover the period of time; in response to receiving a request from a userto locate the specified physical object, scanning, by the system, thephysical space via at least the one or more IR sensors and the one ormore image sensors to identify the specified physical object within thephysical space and to detect a present location of the specifiedphysical object within the physical space; and displaying, by thesystem, to the user, an indication of a location of the specifiedphysical object, wherein the indication comprises at least one of thepresent location of the specified physical object or a predictedlocation of the specified physical object.
 16. The system of claim 15,wherein the monitoring further includes generating a historical movementtrack of the specified physical object based on the collectedcoordinates, wherein the method further comprises: in response tofailing to detect the present location of the specified physical object,generating the predicted location of the specified physical object basedon the historical movement track of the specified physical object,wherein the indication that is displayed to the user is superimposed onan image of the physical space, wherein the indication includes thepredicted location of the specified physical object.
 17. The system ofclaim 16, wherein the indication displayed to the user further includesthe historical movement track and a probability score associated withthe predicted location of the specified physical object.
 18. The systemof claim 16, wherein the plurality of attributes further includes a setof attributes of the specified physical object obtained from an onlinesource, wherein the generating of the predicted location of the objectis further based on the set of attributes obtained from the onlinesource.
 19. The system of claim 16, wherein the plurality of attributesfurther includes dimensions of the specified physical object, whereinthe monitoring of the status of the specified physical object furtherincludes: calculating a barycenter of the specified physical objectbased on the dimensions of the object; measuring a distance between apredetermined location and the barycenter of the specified physicalobject; and transmitting an alert to the user in response to detectingthat the distance is less than a threshold distance, wherein the alertincludes an indication of a location of the specified physical object.20. The system of claim 16, wherein the monitoring of the status of thespecified physical object further includes: monitoring the temperatureof the object over the period of time; and in response to detecting thatthe temperature of the specified physical object is above apredetermined maximum temperature or below a predetermined minimumtemperature, transmitting an alert to the user indicating a location ofthe specified physical object.